Data Mining - Introduction

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1 Data Mining - Introduction Peter Brezany Institut für Scientific Computing Universität Wien Tel Sprechstunde: Di, Outline Business Intelligence and its components Knowledge discovery in databases Data mining techniques - associative and sequence rules - classification -prediction -clustering - neural networks Data warehousing Data webhousing Advanced topics: parallel and distributed data analysis 2 Page 1

2 Literature Mark and Mary Whitehorn: Business Intelligence: The IBM Solution. Springer-Verlag, R. Kimball: The Data Warehouse Toolkit. John Willey, J. Han, M. Kamber: Data Mining. Concepts and Techniques Morgam Kaufmann Publishers, M. Ester, J. Sander: Knowledge Discovery in Databases. Springer-Verlag, I.H. Witten, E. Frank: Data Mining. (Practical Machine Learning Tools and Techniques with Java Implementations). Morgam Kaufmann Publishers, Business Intelligence Definition: Business Intelligence is an umbrella term, broadly covering the processes involved in extracting valuable business information and knowledge from the mass of data that exists within a typical enterprise. 4 Page 2

3 Business Intelligence Tools Data warehouses OLAP (On-Line Analytical Processing) tools Data mining tools Text mining tools the focus of our lectures Web mining tools Data joiners (integrators) Business Intelligence portals, etc. 5 Business Intelligence Tools (cont.) Data warehouse - a repository of multiple heterogeneous data sources, organized under a unified schema at a single site in order to facilitate management decision making. OLAP analysis techniques with functionlities such as summarization, consolidation, and aggregation, as well as the ability to view information from different angles. Data mining extracting or mining knowledge from large data sets. Text mining mining large textual (document) databases. Web mining discovering knowledge from hypertext data. Data joiner - working with data from disparate, heterogeneous data sources Business Intelligence portal a Web site designed to be the first point of entry for visitors to information about a company. With help of the portal s personalising functions, the user can choose information sources that he needs for performing a specific task. 6 Page 3

4 DATA MINING 7 Introduction This lecture topic is about the theme which has come to be known as data mining and knowledge discovery in large databases, data warehouses, and other massive information repositories. Data mining emerged during the late 1980s; has made great strides during the late 1990s, and is expected to continue to flourish into the next future. We introduce interesting data mining techniques and systems, and discuss applications and research directions. Data mining can be viewed as a result of the natural evolution of information technology - including database technology, artificial intelligence, machine learning, neural networks, statistics, patternrecognition, knowledge-based systems, highperformance computing, and data visualization. 8 Page 4

5 What Motivated Data Mining? Why Is It Important? There is the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Applications ranging from business management, production control, and market analysis, to engineering design and science exploration. 9 Business Motivation Medicine Scientific experiments Simulations Data and data exploration cloud Earth observations 10 Page 5

6 CERNs challenge Starting point New detector LHC» Large Hadron Collider, 14 TeV» Goals: Search for Higgs Boson and Graviton (and others) Start 2006 Challenges Data are accessed worldwide» CERN and Regional Centers (Europe, Asia, America)» 2000 users Huge data volumes Data semantics Performance and throughput 11 The LHC Detectors ATLAS CMS LHCb 12 Page 6

7 Multi-Tier Model 13 The Evolution of Database Technology Data Collection and Database Creation (1960s and earlier) - Primitive file processing Database Management Systems (1970s-early 1980s) - Hierarchical, network and relational DB systems - Query languages (SQL, etc), query optimization - Transaction management, concurrency control, recovery -Data modeling tools Advanced Database Systems (mid-1980s-present) object-oriented, object-relational, spatial, multimedia,... Web-based Database Systems (1990s-present) - XML-based DB systems, - Web mining Data Warehousing and Data Mining (late 1980s-present) - Data warehouse and OLAP technology - Data mining and knowledge discovery 14 Page 7

8 Database Querying and Data Mining Query languages like SQL are standardized and powerful, but for not skilled users are they too difficult. OLAP Tools allow flexible multidimensional queries. Their methods are querycentric. Data Warehouse Query languages like SQL OLAP Tools Data Mining Tools 15 We Are Data Rich, But Information Poor 16 Page 8

9 So, What Is Data Mining? Data mining searching for knowledge (interesting patterns) in your data. 17 Data Mining As a Step in the Process of Knowledge Discovery Many people treat data mining as a synonym for the term Knowledge Discovery in Databases, or KDD. Alternative view: data mining as n step in KDD: 1, Data cleaning (to remove noise and inconsistent data) 2. Data integration (where multiple data sources may be combined) 3. Data selection (where data relevant to the analysis task are retrieved from the database) 4. Data transformation (where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance) 5. Data mining (an essential process where intelligent methods are applied in order to extract patterns) 6. Pattern evaluation (to identify the truly interesting patterns representing knowledge based on some interestingness measures) 7. Knowledge presentation to the user 18 Page 9

10 Data Mining in Knowledge Discovery 19 Architecture of a Data Mining System Graphical user interface Pattern evaluation Data mining engine Knowledge base Data cleaning, data integration Database or data warehouse server Filtering Database Data warehouse 20 Page 10

11 Architecture of a Data Mining System (2) Database, data warehouse, or other information repository: One or a set of databases, data warehouses, spreadsheets, etc. Database or data warehouse server: responsible for fetching the relevant data, based on the user s data mining request. Knowledge base: domain knowledge that is used to guide the search, or evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attribute values into different levels of abstraction. Data mining engine: essential to the data mining system; ideally consists of a set of functional modules for tasks such as characterization, association, classification, cluster analysis, and evolution and deviation analysis. 21 Architecture of a Data Mining System (3) Pattern evaluation module: This component typically employs interestingness measures and interacts with the data mining so as to focus the search towards interesting patterns. It may use interestingness thresholds to filter out discovered patterns. Graphical user interface: This module communicates between users and the data mining system allowing the user to specify a data mining query or task provide information to help focus the search perform exploratory data mining based on the intermediate data mining results browse database and data warehouse schemas or data structures evaluate mined patterns visualize the patterns in different forms. 22 Page 11

12 Stages of a Data Exploration Project Based on: Data Preparation for Data Mining, by Dorian Pyle, Morgan Kaufmann Time to complete (percent of total) Importance to success (percent of total) 1. Exploring the problem Exploring the solution Implementation specification Knowledge discovery a. Data preparation b. Data surveying 15 3 c. Data modeling Relational Database A database system, also called a database management system (DBMS), consists of a collection of interrelated data, known as a database, and a set of software programs to manage and access the data. A relational database is a collection of tables, each of which is assigned a unique name. Each table consists of a set of attributes (columns or fields) and usually stores a large set of tuples (records or rows). Each tuple represents an object identified by a unique key. Relational data can be accessed by database queries written in a relational query language, such as SQL. Using data mining, one can search for trends or data patterns in relational databases. 24 Page 12

13 Relational Databases Example The AllElectronics company is described by the following table: customer, item, employee, and branch. Fragments of these tables are shown on the next slide; the attribute that represents the key or composite key component is underlined. The relation customer consists of a set of attributes, including a unique customer identity number (cust_id), and so on. Tables can also be used to represent the relationships between or among multiple relational tables. E.g., these include purchases (customer purchases items, creating a sales transaction that is handled by an employee), items_sold (lists the items sold in the given transaction), and works_at (employee works at a branch of AllElectronics). 25 Fragments of Relations from AllElectronics DB 26 Page 13

14 Data Warehouses A data warehouse is a repository of information collected from multiple sources, stored under a unified schema, and which usually resides at a single site. Data warehouses are constructed via a process of data cleaning, data transformation, data integration, data loading and periodic data refreshing. Figure on the next slide shows the basic architecture of a data warehouse for AllElectronics. In order to facilitate decision making, the data in a data warehouse are organized around major subjects, such as customer, item, supplier, and activity. The data are stored from a historical perspective and are typically summarized. 27 Architecture of a Data Warehouse Data source in Ch. Client Data source in NY Clean Transform Integrate Load Data warehouse Query and analysis tools Data source in T. Client Load = periodical data refreshing Data source in Vancouver Remarks: Ch - Chicago, NY - New York, T - Toronto 28 Page 14

15 Modeling a Data Warehouse A data warehouse is usually modeled by a multidimensional database structure, where each dimension corresponds to an attribute in the schema, each cell stores the value of some aggregate measure, such as count or sales_amount. The actual physical structure of a data warehouse may be a relational data store or a multidimensional data cube. It provides a multidimensional view of data and allows the precomputation and fast accessing of summarized data. Example: A data cube for summarized sales data of AllElectronics is presented in the next slide. 29 A Multidimensional Data Cube 30 Page 15

16 Modeling a Data Warehouse (2) Data warehouse vs. Data mart: A data warehouse collects information about subjects and span an entire organization, and thus its scope is enterprise wide. A data mart is a department-wide. Data warehouse systems are well suited for On-Line Analytical processing, or OLAP. OLAP operations allow the presentation of data at different levels of abstractions. Examples of OLAP operations include drill-down and roll-up, which allow the user to view the data at different degrees of summarization as illustrated in the previous slide. 31 Transactional Databases A transactional database consists of a file where each record represents a transaction. A transaction includes a unique transaction identity number (trans_id), and a list of the items making up the transaction (such as items purchased in a store). The transactional database may have additional tables associated with it, which contain other information regarding the sale, such as the date of the transaction, the custommer ID number, the ID number of the sales person, etc. Example: Transactions can be stored in a table, with one record per transaction. A fragment of a transactional database for AllElectronics is shown in the next slide. 32 Page 16

17 Transactional Databases (2) Trans_id list of item_ids T100 I1, I3, I8, I The transactional database is usually either stored in a flat file in a format similar to that of the above table, or unfolded into a standard relation in a format similar to that of the items_sold table in slide no. 18. A regular data retrieval system is not able to answer queries like Which items sold well together? 33 Advanced Database Systems and Database Applications Relational DB systems have been widely used in business applications. The new database applications include handling spatial data (e.g. maps) engineering design data (e.g., the design of buildings or integrated circuits) hypertext and multimedia data (text, image, video, audio data) time-related data (e.g. stock exchange data) World Wide Web (a huge, widely distributed information repository made available by the Internet) 34 Page 17

18 Data Mining Tasks 35 Data Mining Functionalities - What Kinds of Patterns Can be Minded? Data mining functionalities are used to specify the kind of patterns that can be found in data mining tasks. Data mining tasks can be classified into 2 categories: Descriptive - they characterize the general properties of the data in the database. Prescriptive - they perform inference on the current data in order to make predictions. In some cases, users may have no idea which kinds of patterns may be interesting => searching for several different kinds of patterns in parallel. Data mining systems should be able to discover patterns at various granularities (abstraction levels). Specifying hints to guide or focus the search. 36 Page 18

19 Association Analysis Association analysis is the discovery of association rules showing attribute-value conditions that occur frequently in a given set of data. The association rule X => Y is interpreted as database tuples that satisfy the conditions in X are also likely to satisfy the conditions in Y. Example A data mining system may find in AllElectronics: age(x, ) and income(x, 20K..29K ) => buys(x, CD player ) [support = 2%, confidence = 60%] X is a variable representing a customer. The rule indicates that of the customers under study, 2% are 20 to 29 years of age with an income of 20K to 29K and have purchased a CD player. There is a 60% probability that a customer in this age and income group will purchase a CD player. 37 Association Analysis (Cont.) We would like to determine which items are frequently purchased together within the same transactions. E.g., contains(t, computer ) => contains(t, software ) [support = 1%, confidence = 50%] Explanation: if a transaction, T, contains computer, there is a 50% chance that it contains software as well, and 1% of all of the transactions contain both. This rule involves a single attribute or predicate (i.e. contains) => single-dimensional association rule. It can be written simpy as computer => software {1%,50%] Remark: On the last slide, we have: multi-dimensional assoc. rule. 38 Page 19

20 Classification and Prediction Classification is the process of finding a set of models (or functions) that describe and distinguish data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The derived model is based on the analysis of a training data (i.e., data objects whose class label is known), How is the derived model presented? Classification (IF-THEN) rules Mathematical formulae Decision tree - it is a flow-chart-like tree structure, where each node denotes a test on an attribute value, each branch represents an outcome of the test, and the tree leaves represent classes or class distributions. Neural networks - a collection of neuron-like processing units with weighted connections between the units. 39 Classification and Prediction (Cont.) Prediction - in many applications, users may wish to predict some missing or unavailable data values rather then class labels. The predicted values are usually numerical data. 40 Page 20

21 A Sample Data Set Fictional data set that describes the weather conditions for playing some unspecified game. outlook temperat. humidity rainy play outlook temperat. humidity rainy play sunny hot high false no sunny hot high true no overcast hot high false yes rainy mild high false yes rainy cool normal false yes rainy cool normal true no overcast cool normal true yes sunny mild high false no sunny cool normal false yes rainy mild normal false yes sunny mild normal true yes overcast mild high true yes overcast hot normal false yes rainy mild high true no 41 Learning Rules Example for a set of rules learned from the example data set: if outlook = sunny and humidity = high then play = no if outlook = rainy and windy = true if outlook = overcast if humidity = normal if none of the above then play = no then play = yes then play = yes then play = yes These are classification rules that assign an output class (play or not) to each instance (single example in a data set). 42 Page 21

22 Learning Decision Trees 43 Classification learning Training set: set of examples, where each example is a feature vector (i.e., a set of (attribute,value) pairs) with its associated class. Model is built on this set. Test set: a set of examples disjoint from the training set, used for testing the accuracy of a model. 44 Page 22

23 Slide author: J. Han 45 Slide author: J. Han 46 Page 23

24 Cluster Analysis Clustering analyzes data objects without consulting a known class label. Clustering can be used to generate such labels. The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Each cluster can be viewed as a class of objects, from which rules can be derived. Example Cluster analysis can be performed on AllElectronics customer data in order to identify homogeneous subpopulations of customers. These clusters may represent individual target groups for marketing. (Figure on the next slide shows a 2-D plot of customers with respect to customer locations in a city). 47 Cluster Analysis - Example A 2-D plot of customer data with respect to customer locations in a city, showing 3 data clusters. Each cluster center is marked with a Page 24

25 Outlier Analysis A database may contain data objects that do not comply with the general behaviour or model of the data. These data objects are outliers, Most data mining methods discard outliers as noise or exceptions. In some applications such as fraud detection, the rare events can be more interesting than the more regularly occurring ones, Example Outlier analysis may uncover fraudulent usage of credit cards by detecting purchases of extremely large amounts for a given account number in comparison to regular charges incurred by the same account. 49 On-Line Demo on Clustering torial_html/appletkm.html 50 Page 25

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